{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:54Z","timestamp":1772138094486,"version":"3.50.1"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"18","license":[{"start":{"date-parts":[[2019,2,15]],"date-time":"2019-02-15T00:00:00Z","timestamp":1550188800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,9,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Patient stratification methods are key to the vision of precision medicine. Here, we consider transcriptional data to segment the patient population into subsets relevant to a given phenotype. Whereas most existing patient stratification methods focus either on predictive performance or interpretable features, we developed a method striking a balance between these two important goals.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We introduce a Bayesian method called SUBSTRA that uses regularized biclustering to identify patient subtypes and interpretable subtype-specific transcript clusters. The method iteratively re-weights feature importance to optimize phenotype prediction performance by producing more phenotype-relevant patient subtypes. We investigate the performance of SUBSTRA in finding relevant features using simulated data and successfully benchmark it against state-of-the-art unsupervised stratification methods and supervised alternatives. Moreover, SUBSTRA achieves predictive performance competitive with the supervised benchmark methods and provides interpretable transcriptional features in diverse biological settings, such as drug response prediction, cancer diagnosis, or kidney transplant rejection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The R code of SUBSTRA is available at https:\/\/github.com\/sahandk\/SUBSTRA.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz112","type":"journal-article","created":{"date-parts":[[2019,2,15]],"date-time":"2019-02-15T09:12:08Z","timestamp":1550221928000},"page":"3263-3272","source":"Crossref","is-referenced-by-count":4,"title":["SUBSTRA: Supervised Bayesian Patient Stratification"],"prefix":"10.1093","volume":"35","author":[{"given":"Sahand","family":"Khakabimamaghani","sequence":"first","affiliation":[{"name":"School of Computing Science, Simon Fraser University , Burnaby, BC, Canada"}]},{"given":"Yogeshwar D","family":"Kelkar","sequence":"additional","affiliation":[{"name":"Computational Systems Immunology, Pfizer Worldwide R&D , Cambridge, MA, USA"}]},{"given":"Bruno M","family":"Grande","sequence":"additional","affiliation":[{"name":"Department of Molecular Biology and Biochemistry, Simon Fraser University , Burnaby, BC, Canada"},{"name":"Genome Sciences Centre, British Columbia Cancer Agency , Vancouver, BC, Canada"}]},{"given":"Ryan D","family":"Morin","sequence":"additional","affiliation":[{"name":"School of Computing Science, Simon Fraser University , Burnaby, BC, Canada"},{"name":"Department of Molecular Biology and Biochemistry, Simon Fraser University , Burnaby, BC, Canada"},{"name":"Genome Sciences Centre, British Columbia Cancer Agency , Vancouver, BC, Canada"}]},{"given":"Martin","family":"Ester","sequence":"additional","affiliation":[{"name":"School of Computing Science, Simon Fraser University , Burnaby, BC, Canada"}]},{"given":"Daniel","family":"Ziemek","sequence":"additional","affiliation":[{"name":"Computational Systems Immunology, Pfizer Worldwide R&D , Berlin, Germany"}]}],"member":"286","published-online":{"date-parts":[[2019,2,15]]},"reference":[{"key":"2023013108051481100_btz112-B1","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1186\/s12859-015-0614-0","article-title":"Uncles: method for the identification of genes differentially consistently co-expressed in a specific subset of datasets","volume":"16","author":"Abu-Jamous","year":"2015","journal-title":"BMC Bioinformatics"},{"key":"2023013108051481100_btz112-B2","doi-asserted-by":"crossref","first-page":"3558","DOI":"10.1093\/bioinformatics\/btx464","article-title":"Towards clinically more relevant dissection of patient heterogeneity via survival-based Bayesian clustering","volume":"33","author":"Ahmad","year":"2017","journal-title":"Bioinformatics"},{"key":"2023013108051481100_btz112-B3","doi-asserted-by":"crossref","first-page":"i455","DOI":"10.1093\/bioinformatics\/btw433","article-title":"Drug response prediction by inferring pathway-response associations with kernelized Bayesian matrix factorization","volume":"32","author":"Ammaduddin","year":"2016","journal-title":"Bioinformatics"},{"key":"2023013108051481100_btz112-B4","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/nature11003","article-title":"The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity","volume":"483","author":"Barretina","year":"2012","journal-title":"Nature"},{"key":"2023013108051481100_btz112-B5","doi-asserted-by":"crossref","first-page":"2773","DOI":"10.1158\/1535-7163.MCT-15-0243","article-title":"Mek inhibitor selumetinib (azd6244; arry-142886) prevents lung metastasis in a triple-negative breast cancer xenograft model","volume":"14","author":"Bartholomeusz","year":"2015","journal-title":"Mol. 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